论文标题
轨迹 - 用户链接比您想象的要容易
Trajectory-User Linking Is Easier Than You Think
论文作者
论文摘要
轨迹 - 用户链接（TUL）是一项相对较新的移动性分类任务，其中匿名轨迹链接到生成它们的用户。随着从个性化建议到犯罪活动检测的申请，在过去五年中，TUL受到了越来越多的关注。尽管研究主要集中于学习深层表示，以捕获个人用户独有的复杂时空流动性模式，但我们证明，访问模式在用户中是非常独特的，因此直接应用于原始数据的简单启发式学足以求解图尔。更具体地说，我们证明，每个轨迹的一次入住足以正确预测用户的身份，多达85％的时间。此外，通过使用非参数分类器，我们将其扩展到超过100K用户，这比最先进的数量级增加了三个数量级。对四个现实世界数据集（Brightkite，Foursquare，Gowalla和Weeplaces）进行了广泛的经验分析将我们的发现与最先进的结果进行了比较，更重要的是验证了我们的说法比通常所相信的要容易得多。
Trajectory-User Linking (TUL) is a relatively new mobility classification task in which anonymous trajectories are linked to the users who generated them. With applications ranging from personalized recommendations to criminal activity detection, TUL has received increasing attention over the past five years. While research has focused mainly on learning deep representations that capture complex spatio-temporal mobility patterns unique to individual users, we demonstrate that visit patterns are highly unique among users and thus simple heuristics applied directly to the raw data are sufficient to solve TUL. More specifically, we demonstrate that a single check-in per trajectory is enough to correctly predict the identity of the user up to 85% of the time. Moreover, by using a non-parametric classifier, we scale up TUL to over 100k users which is an increase over state-of-the-art by three orders of magnitude. Extensive empirical analysis on four real-world datasets (Brightkite, Foursquare, Gowalla and Weeplaces) compares our findings to state-of-the-art results, and more importantly validates our claim that TUL is easier than commonly believed.
